In Core E we will perform two major tasks. One task is the development of new analytic models for dealing with longitudinal data from both national and local-area studies of elderly and oldest-old populations. The second task is the investigation of statistical models for masking crucial variables in a data set to prevent individuals from being identified from data sets-yet generate data sets that retain considerable and sufficient detail for multiple research purposes. In fact, on a fundamental mathematical level these two analytic tasks are related-though possibly as inverse problems. In analyzing longitudinal data for human populations the assessments of the age trajectories of health and functional changes are generally "incomplete," i.e., measurements occur at fixed times often several years apart. Thus there is considerable "missing data" on the processes being sampled in most longitudinal observation plans. The data masking problem is the reverse problem, i.e., what data has to be "masked" or made "missing" to prevent individuals from being identified. Thus, the measurement can either be removed or made "fuzzier" by reporting less detailed measurement intervals or by adding random noise to the variable. We will start by creating models with the basics of the Missing Information principle, the super-population theory of sampling (appropriate for modeling processes, the E-M algorithm and models of partly observed stochastic processes, and apply those models and concepts to different types of data (i.e., survey reports, medical cost data, genetic or DNA data) to see how to best proceed in a..) Analysis, and b.) Masking, with each. It is anticipated, that apart from the standard approaches to assessing confidentiality (e.g., as practiced by NCHS or RTI; see Core C), that multivariate procedures will be explored for data masking that are based on stochastic process models, and that can use mathematical and statistical principles to approximate multivariate distributions to the degree desired.